--- title: Vector Space Wall link: url: https://testimonial.to/qdrant/all text: Submit Your Testimonial testimonials: - id: 0 name: Jonathan Eisenzopf position: Chief Strategy and Research Officer at Talkmap avatar: src: /img/customers/jonathan-eisenzopf.svg alt: Avatar text: “With Qdrant, we found the missing piece to develop our own provider independent multimodal generative AI platform on enterprise scale.” - id: 1 name: Angel Luis Almaraz Sánchez position: Full Stack | DevOps avatar: src: /img/customers/angel-luis-almaraz-sanchez.svg alt: Avatar text: Thank you, great work, Qdrant is my favorite option for similarity search. - id: 2 name: Shubham Krishna position: ML Engineer @ ML6 avatar: src: /img/customers/shubham-krishna.svg alt: Avatar text: Go ahead and checkout Qdrant. I plan to build a movie retrieval search where you can ask anything regarding a movie based on the vector embeddings generated by a LLM. It can also be used for getting recommendations. - id: 3 name: Kwok Hing LEON position: Data Science avatar: src: /img/customers/kwok-hing-leon.svg alt: Avatar text: Check out qdrant for improving searches. Bye to non-semantic KM engines. - id: 4 name: Ankur S position: Building avatar: src: /img/customers/ankur-s.svg alt: Avatar text: Quadrant is a great vector database. There is a real sense of thought behind the api! - id: 5 name: Yasin Salimibeni View Yasin Salimibeni’s profile position: AI Evangelist | Generative AI Product Designer | Entrepreneur | Mentor avatar: src: /img/customers/yasin-salimibeni-view-yasin-salimibeni.svg alt: Avatar text: Great work. I just started testing Qdrant Azure and I was impressed by the efficiency and speed. Being deploy-ready on large cloud providers is a great plus. Way to go! - id: 6 name: Marcel Coetzee position: Data and AI Plumber avatar: src: /img/customers/marcel-coetzee.svg alt: Avatar text: Using Qdrant as a blazing fact vector store for a stealth project of mine. It offers fantasic functionality for semantic search ✨ - id: 7 name: Andrew Rove position: Principal Software Engineer avatar: src: /img/customers/andrew-rove.svg alt: Avatar text: We have been using Qdrant in production now for over 6 months to store vectors for cosine similarity search and it is way more stable and faster than our old ElasticSearch vector index.

No merging segments, no red indexes at random times. It just works and was super easy to deploy via docker to our cluster.

It’s faster, cheaper to host, and more stable, and open source to boot! - id: 8 name: Josh Lloyd position: ML Engineer avatar: src: /img/customers/josh-lloyd.svg alt: Avatar text: I'm using Qdrant to search through thousands of documents to find similar text phrases for question answering. Qdrant's awesome filtering allows me to slice along metadata while I'm at it! 🚀 and it's fast ⏩🔥 - id: 9 name: Leonard Püttmann position: data scientist avatar: src: /img/customers/leonard-puttmann.svg alt: Avatar text: Amidst the hype around vector databases, Qdrant is by far my favorite one. It's super fast (written in Rust) and open-source! At Kern AI we use Qdrant for fast document retrieval and to do quick similarity search for text data. - id: 10 name: Stanislas Polu position: Software Engineer & Co-Founder, Dust avatar: src: /img/customers/stanislas-polu.svg alt: Avatar text: Qdrant's the best. By. Far. - id: 11 name: Sivesh Sukumar position: Investor at Balderton avatar: src: /img/customers/sivesh-sukumar.svg alt: Avatar text: We're using Qdrant to help segment and source Europe's next wave of extraordinary companies! - id: 12 name: Saksham Gupta position: AI Governance Machine Learning Engineer avatar: src: /img/customers/saksham-gupta.svg alt: Avatar text: Looking forward to using Qdrant vector similarity search in the clinical trial space! OpenAI Embeddings + Qdrant = Match made in heaven! - id: 12 name: Rishav Dash position: Data Scientist avatar: src: /img/customers/rishav-dash.svg alt: Avatar text: awesome stuff 🔥 sitemapExclude: true ---